Service providers and device manufacturers (e.g., wireless, cellular, etc.) are continually challenged to deliver value and convenience to consumers by, for example, providing compelling network services. One area of interest has been development of applications and services, which process and/or utilize various types of data in order to provide accurate, appropriate, and updated information. In particular, these applications and services can include processing and analyzing data utilized, for example, by social networking services, navigation services, search engines, content providers, and the like. Traditionally, data processing and analysis may be implemented via one or more servers (or nodes) and/or clusters of servers (or nodes) that provide, for instance, distributed computing and/or data storage to support the services. Moreover, such data processing and analysis historically have been segregated based on whether the data being processed is “slow moving” data (e.g., static or relatively static data that is collected over long periods of time such as user behavior data, service usage information, etc. processed by analytics systems) or whether the data is collected as data streams in real-time (e.g., social networking feeds, location tracking feeds, etc. processed by stream processing systems). Each different type of data (e.g., slow moving vs. real-time) has had different data processing architectures and techniques that traditionally have been used in isolation. As a result, service providers and device manufacturers face significant technical challenges to managing and/or integrating the data processing and analysis methods for slow moving data with the data processing and analysis methods for real-time data.